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Optimization of High-Pressure-Assisted Extraction of Cadmium and Lead from Kelp (Laminaria japonica) Using Response Surface Methodology.
Wang, Hao; Wang, Qiang; Zhu, Jiahong; Hu, Guixian.
Afiliación
  • Wang H; Institute of Agro-Product Safety and Nutrition, Zhejiang Academy of Agricultural Sciences, 298 Deshengzhong Road, Hangzhou 310021, China.
  • Wang Q; Institute of Agro-Product Safety and Nutrition, Zhejiang Academy of Agricultural Sciences, 298 Deshengzhong Road, Hangzhou 310021, China.
  • Zhu J; Institute of Agro-Product Safety and Nutrition, Zhejiang Academy of Agricultural Sciences, 298 Deshengzhong Road, Hangzhou 310021, China.
  • Hu G; Institute of Agro-Product Safety and Nutrition, Zhejiang Academy of Agricultural Sciences, 298 Deshengzhong Road, Hangzhou 310021, China.
Foods ; 11(7)2022 Apr 02.
Article en En | MEDLINE | ID: mdl-35407123
ABSTRACT
Kelp (Laminaria japonica) is a popular and nutritious sea vegetable, but it has a strong biosorption capacity for heavy metals. The high content of cadmium (Cd) and lead (Pb) is a threat to the quality of kelp. The objective of this study was to investigate the effects of high-pressure-assisted extraction (HPAE) conditions on Cd and Pb removal efficiency from kelp. Pressure intensity (0.1-200 MPa), the number of HPAE cycles (one to five) and acetic acid concentration (0-10%) were optimized using response surface methodology. The pressure intensity had the most significant positive effects on Cd and Pb removal efficiency, while the correlation between acetic acid concentration and removal efficiency was positive for Cd and negative for Pb. The optimum conditions for the removal of Cd and Pb were attained at 188 MPa, with four cycles and with an acetic acid concentration of 0%. At optimum conditions, the experimental values of removal efficiency were 61.14% (Cd) and 70.97% (Pb), and this was consistent with the predicted value, confirming the validity of the predictive model.
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Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Foods Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Foods Año: 2022 Tipo del documento: Article País de afiliación: China